Ablation study.
As illustrated in Table REF , we find that each dimension i.e., \(\#\)\({\mathsf {CC}}\) , \({\mathsf {GS}}\) , \({\mathsf {GPR}}\) , \({\mathsf {GPPR}}\) , \({\mathsf {UGT}}\) , or \({\mathsf {IGT}}\) , is less effective than \({\mathsf {SIT}}\) . For both behaviors and datasets, \({\mathsf {UGT}}\)  or \({\mathsf {GPR}}\)  performs as the best competitor in all evaluation metrics, compared with both other \({\mathsf {SIT}}\) -based dimensions and all competitors in Table REF . Regarding \({\mathsf {IGT}}\) , it is less effective than \({\mathsf {UGT}}\)  and \({\mathsf {GPR}}\) , but is still comparable to the existing measures in Table REF . For instance, the \({\mathsf {IGT}}\)  score is 4.3%, 5.6% better than the group-level competitor \({\mathsf {GD}}\)  in terms of AUC and accuracy for adoption behaviors of MOBA-A, as \({\mathsf {IGT}}\)  additionally involves the similarities among in-group members.
Notice that both \(\#\)\({\mathsf {CC}}\)  and \({\mathsf {GS}}\)  scores are analyzed in the work of structural diversity [1]} and can be treated as competitors of other \({\mathsf {SIT}}\) -based measures. In particular, we assemble both measures into \({\mathsf {XGBoost}}\)  and denote the model instance as \(\#\)\({\mathsf {CC}}\) -\({\mathsf {GS}}\) , whose scores can extensively outperform those of each single measure \(\#\)\({\mathsf {CC}}\)  and \({\mathsf {GS}}\)  as reported in Table REF . However, \(\#\)\({\mathsf {CC}}\) -\({\mathsf {GS}}\)  are still worse than some single dimensions (i.e., \({\mathsf {GPR}}\)  or \({\mathsf {UGT}}\) ) and the proposed \({\mathsf {SIT}}\) .
Regarding the reasons for the performance of each dimension in \({\mathsf {SIT}}\) , we leave them in the analysis parts.
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